outlier filtering

inference.py

@@ -11,6 +11,23 @@ import pandas as pd
 import plotly.express as px
 import matplotlib.pyplot as plt
 
+def remove_outliers(point_cloud, threshold=3.0):
+    # Calculate mean and standard deviation along each dimension
+    mean = np.mean(point_cloud, axis=0)
+    std = np.std(point_cloud, axis=0)
+    
+    # Define lower and upper bounds for each dimension
+    lower_bounds = mean - threshold * std
+    upper_bounds = mean + threshold * std
+    
+    # Create a boolean mask for points within the bounds
+    mask = np.all((point_cloud >= lower_bounds) & (point_cloud <= upper_bounds), axis=1)
+    
+    # Filter out outlier points
+    filtered_point_cloud = point_cloud[mask]
+    
+    return filtered_point_cloud
+
 
 def map_image_range(depth, min_value, max_value):
     """
@@ -239,6 +256,7 @@ class DepthPredictor:
             points, _ = PCL(mask, depth)
             idxs = np.random.choice(len(points), int(n_samples))
             points = points[idxs]
+            points = remove_outliers(points)
             for point in points:
                 cube = o3d.geometry.TriangleMesh.create_box(
                     width=cube_size, height=cube_size, depth=cube_size